Ecological genetics

BACKGROUND: The efficiency of modern selection programs in poultry breeding largely depends on animal genotyping. The availability of genotyping data allows to perform genome-wide association studies (GWAS), a genotype array analysis that identifies relationships between phenotypic traits and genome. Establishing local poultry breeds for meat production, a crucial protein source for human nutrition, is a significant priority within the national poultry sector. Achieving this goal requires examination of available genetic resources and identification of genomic regions responsible for manifestation of meat productivity.

AIM: the present research aimed to perform a GWAS for carcass traits in Tsarskoye Selo chicken breed to establish the genetic determinants of meat productivity.

METHODS: Tsarskoye Selo chicken breed (n = 96) was used as material for the study. Genotyping data were obtained using the Illumina Chicken 60K SNP iSelectBeadChip (Illumina Inc., USA), and GWAS was performed using EMMAX with Bonferroni correction. Genome-wide significance was assessed using the simple method in R, the calculation of the effective number of independent tests was performed using the Meff program. Gene annotation was performed via ENSEMBL genome browser, using GRCg6a genome assembly.

RESULTS: For 8 out of 12 traits, 11 suggestive SNPs (2,31E-05) were obtained on chromosomes 1,3,11,12,15,22,23 and 27. The highest number of SNPs was detected for the thigh muscles (TM)—3 SNPs, and for breast muscles (BM)—2 SNPs. For the remaining traits, 1 SNP each was detected. A total of 16 genes associated with immunity (SKAP1, DCAF1, ISCU, TRAFD1), metabolism (GPATCH1, CMKLR1, TBC1D15, RAB21), osteogenesis (GPM6B, RAB9A, TRAPPC2), protein synthesis (RPL6), serotonin biosynthesis and eating behavior (TPH2), myogenesis (AGO3), morphogenesis (UNC5D), and DNA damage response (CLSPN) were identified.

CONCLUSION: The obtained results can be successfully used in selection programs of Tsarskoye Selo chicken breed, and can be recommended for approbation in other breeds.

BACKGROUND: Experimental data on tissue-specific effects and antigenotoxic potential facilitate more targeted practical applications of antigenotoxicants.

AIM: This work aimed to assess the tissue-specific antigenotoxic activity of the natural flavonoids apigenin, naringenin, and hesperetin against the DNA-damaging effects of temozolomide and the cytogenetic effects of genotoxicants with various mechanisms of action.

METHODS: The genotoxicants (temozolomide 50 mg/kg, cyclophosphamide 20 mg/kg, methyl methanesulfonate 80 mg/kg, and dioxydin 250 mg/kg) were administered intraperitoneally to mice. Before genotoxicant injections, three oral doses of apigenin (5, 25, and 50 mg/kg) and naringenin and hesperetin (25, 50, and 100 mg/kg) were given. In a separate experiment, apigenin was administered one hour after temozolomide injection. The study used a comet assay in bone marrow, liver, kidney, brain, and rectum cells, as well as chromosome aberration analysis in bone marrow cells.

RESULTS: Apigenin decreased temozolomide-induced DNA damage in the bone marrow (52%–66%), liver (31%–65%), kidneys (50%), and rectum (100%), but not in the brain. All apigenin doses reduced kidney levels of atypical DNA comets. Naringenin demonstrated antigenotoxic activity by reducing DNA damage in the bone marrow (48%–62%), brain (26%–44%), and rectum (49%–54%), but not in the liver or kidneys. Hesperetin showed antigenotoxic effects in the bone marrow (23%), kidneys (29%–33%), brain (23%–42%), and rectum (32%–47%). In a cytogenetic assay, apigenin, naringenin, and hesperetin dose-dependently reduced the effects of temozolomide by 49%–73%, 49%–75%, and 39%–55%, respectively. In the post-treatment mode, apigenin 5–50 mg/kg reduced the effects of temozolomide by 47%–51%. Apigenin 5 mg/kg and 25 mg/kg markedly reduced the cytogenetic effects of dioxydin, while apigenin 25 mg/kg decreased those of cyclophosphamide and methyl methanesulfonate. Naringenin 50 mg/kg and 100 mg/kg reduced the effect of cyclophosphamide (43%–71%), but not dioxydin.

CONCLUSION: Apigenin, naringenin, and hesperetin show tissue-specific antigenotoxic activity against the effects of temozolomide. Apigenin and naringenin reduce the cytogenetic effects of genotoxicants with various mechanisms of damaging action. The findings highlight the potential of the investigated flavonoids as genome-protecting agents with a possible targeted action.

The intensive application of antimicrobial agents in industrial poultry farming contributes to the formation and maintenance of an extensive resistome – the collection of antibiotic resistance genes within microbial communities. This review synthesizes current data on the structure, diversity, and circulation of antibiotic resistance genes in poultry production systems within the context of the “One Health” concept. It offers a unique, systemic perspective, viewing a poultry farm as an integrated ecosystem for the circulation of resistance genes.

Metagenomic studies have revealed over 600 types of resistance genes in the poultry microbiome, conferring resistance to 25 classes of antibiotics. The most prevalent genes confer resistance to tetracyclines (tetA, tetB, tetM), β-lactams (bla_TEM, bla_CTX-M, bla_CMY-2), macrolides (ermB, ermA), fluoroquinolones (qnrS, qnrB), and aminoglycosides. Of particular concern is the detection of carbapenemase genes (bla_NDM, bla_OXA-48) and genes conferring resistance to last-resort drugs such as tigecycline (tetX4) and colistin (mcr-1). The concentration of resistance genes in poultry litter can reach 10¹⁶ copies per gram, exceeding levels found in other types of livestock waste.

The review details key ecological reservoirs, including the gut microbiome, hatcheries, biofilms in water systems, litter, and production surfaces. The primary dissemination mechanisms encompass vertical transmission via hatcheries, horizontal gene transfer mediated by plasmids and transposons, and large-scale dispersion through litter into agro-ecosystems. The role of co-selection with heavy metal and biocide resistance genes in maintaining the resistome in the absence of antibiotic pressure is highlighted.

This review emphasizes the necessity for an integrated approach to resistome control. This includes optimizing antimicrobial use, enhancing biosecurity measures, developing alternative prophylactic strategies, and implementing effective waste management protocols to mitigate environmental and epidemiological risks.

BACKGROUND: Guar (Cyamopsis tetragonoloba), an industrially important crop, is valued for the galactomannan gum derived from its seeds. Recent advances in genomic and transcriptomic research have provided valuable resources such as the reference genome and several sets of gene expression profiles. However, these data are currently fragmented and therefore require bioinformatics expertise to access and analyze them. Additionally, several genomic assemblies have been recently published, but there are currently no bioinformatics platforms specifically dedicated to guar genomics and transcriptomics.

AIM: To address this challenge, we have developed CTGA, a comprehensive functional genomic web portal for guar.

METHODS: Using Flask, as well as popular Python, CSS, and HTML libraries, we have developed a backend and frontend for the genomic platform.

RESULTS: We have performed a de novo structural and functional annotation of the guar genome predicting 57,019 protein-coding genes with UTRs. Besides, expression data from 85 public RNA-seq libraries representing various tissues and conditions were collected to create a normalized gene expression atlas. CTGA features an intuitive web interface to provide interactive tools, including a genome browser (IGV), BLAST for homology searching, tools for the Gene Ontology enrichment analysis, for working with guar genomic sequences, as well as a tool for generating heatmaps for more convenient analysis of guar gene expression in various tissues and experimental conditions. It also includes detailed functional annotations from various sources (eggNOG, Mercator4, GO, and KEGG) and instant visualization of gene expression profiles.

CONCLUSION: CTGA is available at: https://guar.arriam.ru/